Estimating 3D Object Parameters from 2D Grey-Level Images

This thesis describes a general framework for parameter estimation, which is suitable for computer vision applications. The approach described combines 3D modelling, animation and estimation tools to determine parameters of objects in a scene from 2D grey-level images. The animation tool predicts images using a 3D model of the scene (virtual reality), describing components like cameras, light sources and objects, and their parameters. The 3D modelling, using primitives like quadrics enables the handling of occlusion. A least squares estimator in combination with the modelling tool is used to estimate the selected parameters from real or animated grey-level images. The non-linear relation between the measurements and the set of parameters is coped with by the iterative application of the linear estimator. The least squares estimation paradigm is applied in a standardised way to the grey-level images of objects by considering the pixels as measurements of the object parameters. Two or more images, even when taken from different points of view, can be included simply by extending the measurement vector. Also the inclusion of a Gaussian filter, to increase the estimator performance by improving the image properties, can be carried out in a natural way

A New Method for Fast Computation of Moments Based on 8-neighbor Chain CodeApplied to 2-D Objects Recognition

2D moment invariants have been successfully applied in pattern recognition tasks. The main difficulty of using moment invariants is the computational burden. To improve the algorithm of moments computation through an iterative method, an approach for fast computation of moments based on the 8-neighbor chain code is proposed in this paper. Then artificial neural networks are applied for 2D shape recognition with moment invariants. Compared with the method of polygonal approximation, this approach shows higher accuracy in shape representation and faster recognition speed in experiment

Control system for a superconducting rectifier using a microcomputer

Within the scope of a research program of superconducting rectifiers software is being developed to take care of the control of such systems. The hard-ware architecture which interferes with the in and output signals is based on a LSI-11/2 microprocessor with sufficient mass storage for data logging, console and printer. The flexibility inherent to this hardware configuration is desired for optimalisation of the rectifier concerning maximum current, power, efficiency and quench stability. The paper describes the structure of the program and the interaction between both computer hardware and software and the superconducting rectifier. However, because the reliability of computersystems is unsatisfactory an additional hardware protection system still handles the most important alarms

Radiological Physics

Artificial neural networks (ANN), especially, multilayer perceptrons (MLP) have been widely used in pattern recognition and classification. Nevertheless, how to incorporate a priori knowledge in the design of ANNs is still an open problem. The paper tries to give some insight on this topic emphasizing weight initialization from three perspectives. Theoretical analyses and simulations are offered for validatio